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Simulation of Longitudinal Exposure Data with Variance-Covariance Structures Based on Mixed Models
Citation:
Song, P., J. XUE, AND Z. Li. Simulation of Longitudinal Exposure Data with Variance-Covariance Structures Based on Mixed Models. RISK ANALYSIS. Blackwell Publishing, Malden, MA, 33(3):469-479, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Longitudinal data are important in exposure and risk assessments, especially for pollutants with long half-lives in the human body and where chronic exposures to current levels in the environment raise concerns for human health effects. It is usually difficult and expensive to obtain large longitudinal data sets for human exposure studies. This article reports a new simulation method to generate longitudinal data with flexible numbers of subjects and days. Mixed models are used to describe the variance-covariance structures of input longitudinal data. Based on estimated model parameters, simulation data are generated with similar statistical characteristics compared to the input data. Three criteria are used to determine similarity: the overall mean and standard deviation, the variance components percentages, and the average autocorrelation coefficients. Upon the discussion of mixed models, a simulation procedure is produced and numerical results are shown through one human exposure study. Simulations of three sets of exposure data successfully meet above criteria. In particular, simulations can always retain correct weights of inter- and intrasubject variances as in the input data. Autocorrelations are also well followed. Compared with other simulation algorithms, this new method stores more information about the input overall distribution so as to satisfy the above multiple criteria for statistical targets. In addition, it generates values from numerous data sources and simulates continuous observed variables better than current data methods. This new method also provides flexible options in both modeling and simulation procedures according to various user requirements.
URLs/Downloads:
Risk Analysis
Simulation of Longitudinal Exposure Data with Variance-Covariance Structures Based on Mixed Models (PDF, NA pp, 322 KB, about PDF)